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Record W3158645676 · doi:10.3390/w13081131

Laboratory and In Situ Determination of Hydraulic Conductivity and Their Validity in Transient Seepage Analysis

2021· article· en· W3158645676 on OpenAlexaboutno aff
Soonkie Nam, Marte Gutierrez, Panayiotis Diplas, John Petrie

Bibliographic record

VenueWater · 2021
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic conductivityPermeameterGroundwaterGeotechnical engineeringGroundwater modelHydraulic headGeologyBoreholeSoil scienceSoil testAnisotropyPermeability (electromagnetism)Slug testAquiferGroundwater flowSoil waterChemistry

Abstract

fetched live from OpenAlex

This paper critically compares the use of laboratory tests against in situ tests combined with numerical seepage modeling to determine the hydraulic conductivity of natural soil deposits. Laboratory determination of hydraulic conductivity used the constant head permeability and oedometer tests on undisturbed Shelby tube and block soil samples. The auger hole method and Guelph permeameter tests were performed in the field. Groundwater table elevations in natural soil deposits with different hydraulic conductivity values were predicted using finite element seepage modeling and compared with field measurements to assess the various test results. Hydraulic conductivity values obtained by the auger hole method provide predictions that best match the groundwater table’s observed location at the field site. This observation indicates that hydraulic conductivity determined by the in situ test represents the actual conditions in the field better than that determined in a laboratory setting. The differences between the laboratory and in situ hydraulic conductivity values can be attributed to factors such as sample disturbance, soil anisotropy, fissures and cracks, and soil structure in addition to the conceptual and procedural differences in testing methods and effects of sample size.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.202
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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